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NSR database version of May 24, 2024.

Search: Author = H.J.Du

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2022LI29      Phys.Rev. C 105, 064306 (2022)

C.-Q.Li, C.-N.Tong, H.-J.Du, L.-G.Pang

Deep learning approach to nuclear masses and α-decay half-lives

ATOMIC MASSES Z=8-110, N=8-160, A=16-280; analyzed binding energy/nucleon for 2149 nuclei using nonlinear transformation and feature representation capability of deep neural network (DNN), and results compared with predictions of liquid-drop model (LDM) and evaluated experimental values in AME2020; deduce the importance of augmenting shell structure and magic numbers predicted from finite-range droplet model in this type of regression, and multi-task learning (MTL) task.

RADIOACTIVITY Z=52-116(α); Z=86-93(α); analyzed Q(α) and T1/2 values for 486 α emitters, including N=106-144 for Z=86-93 α emitters, using nonlinear transformation and feature representation capability of deep neural network (DNN) for α-emitters, and results compared with available experimental values, and with three-parameter Gamow formula for 159 even-even α emitters.

doi: 10.1103/PhysRevC.105.064306
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